Genome-Wide Association Study and Cost-Efficient Genomic Predictions for Growth and Fillet Yield in Nile Tilapia (Oreochromis niloticus)

Abstract

Fillet yield (FY) and harvest weight (HW) are economically important traits in Nile tilapia production. Genetic improvement of these traits, especially for FY, are lacking, due to the absence of efficient methods to measure the traits without sacrificing fish and the use of information from relatives to selection. However, genomic information could be used by genomic selection to improve traits that are difficult to measure directly in selection candidates, as in the case of FY. The objectives of this study were: (i) to perform genome-wide association studies (GWAS) to dissect the genetic architecture of FY and HW, (ii) to evaluate the accuracy of genotype imputation and (iii) to assess the accuracy of genomic selection using true and imputed low-density (LD) single nucleotide polymorphism (SNP) panels to determine a cost-effective strategy for practical implementation of genomic information in tilapia breeding programs. The data set consisted of 5,866 phenotyped animals and 1,238 genotyped animals (108 parents and 1,130 offspring) using a 50K SNP panel. The GWAS were performed using all genotyped and phenotyped animals. The genotyped imputation was performed from LD panels (LD0.5K, LD1K and LD3K) to high-density panel (HD), using information from parents and 20% of offspring in the reference set and the remaining 80% in the validation set. In addition, we tested the accuracy of genomic selection using true and imputed genotypes comparing the accuracy obtained from pedigree-based best linear unbiased prediction (PBLUP) and genomic predictions. The results from GWAS supports evidence of the polygenic nature of FY and HW. The accuracy of imputation ranged from 0.90 to 0.98 for LD0.5K and LD3K, respectively. The accuracy of genomic prediction outperformed the estimated breeding value from PBLUP. The use of imputation for genomic selection resulted in an increased relative accuracy independent of the trait and LD panel analyzed. The present results suggest that genotype imputation could be a cost-effective strategy for genomic selection in Nile tilapia breeding programs.

Multi-trait GWAS using high-density genotypes identifies genes associated with body traits in Nile tilapia

“…We found moderate to high heritability values for ADG, BWH, WW, HW, HON, BLH, FW and FY, which is consistent with previous estimates for Nile tilapia calculated using pedigree and genomic methods [8,9,19,20]. The additive genetic variance and heritability estimated for BWH using genotypes imputed to high-density genotypes increased about 15% in comparison to the value previously estimated for the same population using a 50K SNP panel [19].…”

“…The use of genomic information can help in the identification of QTLs controlling complex traits which are economically important for aquaculture purposes, such as growthrelated traits. Previous studies have identified loci and candidate genes associated with growth-related traits in aquaculture species [19,22,23,25,36,37]. However, similar to what we found when using stGWAS ( Supplementary Figure 1), few or no markers surpassed the genome-wide significance threshold or represented a small proportion of genetic variance in GWAS.…”

“…We used a total of 1,309 phenotyped animals from 72 families (mean = 18, minimum = 7, and maximum = 25 animals per family) belonging to a breeding nucleus owned by Aquacorporación International group (ACI), Costa Rica. More details about the breeding program and the origin of the Nile tilapia population is described in detail [17,19,75].…”

“…The availability of a chromosome-level reference genome assembly [14] and highthroughput whole-genome sequencing (WGS) methods [15,16], has allowed for the assessment of genetic variation of different Nile tilapia populations at a genome-wide level and the recent development of single nucleotide polymorphism (SNP) panels [17,18]. The availability of Nile tilapia SNP panels made it possible to use modern molecular breeding approaches; including mapping of quantitative trait loci (QTL) through genome-wide association studies (GWAS), marker-assisted selection (MAS) and genomic selection [19,20]. The GWAS approach evaluates the association between the genotype and the phenotype of interest, with both sources of information available for a large number of individuals.…”

“…This method captures the linkage disequilibrium (LD) between markers and causative mutations that tend to be inherited together across generations [21]. GWAS has been applied to provide insights into both the genetic architecture and loci underpinning the genetic variation of growth-related traits in different aquaculture species, including Atlantic salmon and catfish [22][23][24][25], using high-density SNPs arrays (ranging from 108 K to 218 K SNPs) and, more recently, Nile tilapia by using a medium-density (50 K) SNP array [19].…”